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This research investigates the critical role of data governance (DG) in shaping a data-driven culture (DDC) within organizations, recognizing the transformative potential of data utilization for efficiency, opportunities, and productivity. The study delves into the influence of DG on DDC, emphasizing the mediating effect of data literacy (DL).

The study empirically assesses 125 experienced managers in Indonesian public service sector organizations using a quantitative approach. Structural Equation Modeling (SEM) analysis was chosen to examine the impact of DG on DDC and the mediating effects of DL on this relationship.

The findings highlight that both DG and DL serve as antecedents to DDC, with DL identified as a crucial mediator, explaining a significant portion of the effects between DG and DDC.

Beyond unveiling these relationships, the study discusses practical implications for organizational leaders and managers, emphasizing the need for effective policies and strategies in data-driven decision-making.

This research fills an important research gap by introducing an original model and providing empirical evidence on the dynamic interplay between DG, DL, and DDC, contributing to the evolving landscape of data-driven organizational cultures.

In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation.

On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records.

The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval.

The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems.

The study aims to explore the classification of electronic games in Dewey decimal classification (DDC) and The Library of Congress classification (LCC) schemes.

The study adopted a comparative analytical method to explore the topic in both the DDC and the LCC schemes by comparing its processing method in both schemes. The study measures the extent to which both schemes succeed in allocating notations covering the topic’s literature.

The study reached several results, the most important of which are: the difference between the two main cognitive sections, to which they belong to the topic, namely, arts and recreation (700) in the DDC scheme and the geography section (G) in the LCC scheme, while they were found to share the same sub-section scheme. The two schemes do not allocate notations to address the subject of electronic games as literature and other notations that have not been embodied for electronic games themselves or in the form of a compact disc or other media.

As far as we know, this is the first paper that compares the treatment of video games in DDC and Library of Congress classification schemes. The study allows for understanding the difference in the treatment of topics in both schemes, which would help in the decision of the adoption of a particular classification scheme.

The purpose of this paper is to explore the potential of applying the Dewey Decimal Classification (DDC) as an established knowledge organization system (KOS) for enhancing social tagging, with the ultimate purpose of improving subject indexing and information retrieval.

Over 11,000 Intute metadata records in politics were used. Totally, 28 politics students were each given four tasks, in which a total of 60 resources were tagged in two different configurations, one with uncontrolled social tags only and another with uncontrolled social tags as well as suggestions from a controlled vocabulary. The controlled vocabulary was DDC comprising also mappings from the Library of Congress Subject Headings.

The results demonstrate the importance of controlled vocabulary suggestions for indexing and retrieval: to help produce ideas of which tags to use, to make it easier to find focus for the tagging, to ensure consistency and to increase the number of access points in retrieval. The value and usefulness of the suggestions proved to be dependent on the quality of the suggestions, both as to conceptual relevance to the user and as to appropriateness of the terminology.

No research has investigated the enhancement of social tagging with suggestions from the DDC, an established KOS, in a user trial, comparing social tagging only and social tagging enhanced with the suggestions. This paper is a final reflection on all aspects of the study.

The purpose of this paper is to describe a new approach to a well-known problem for digital libraries, how to search across multiple unrelated libraries with a single query.

The approach involves creating new Dewey Decimal Classification terms and numbers from existing Dublin Core records. In total, 263,550 records were harvested from three digital libraries. Weighted key terms were extracted from the title, description and subject fields of each record. Ranked DDC classes were automatically generated from these key terms by considering DDC hierarchies via a series of filtering and aggregation stages. A mean reciprocal ranking evaluation compared a sample of 49 generated classes against DDC classes created by a trained librarian for the same records.

The best results combined weighted key terms from the title, description and subject fields. Performance declines with increased specificity of DDC level. The results compare favorably with similar studies.

The metadata harvest required manual intervention and the evaluation was resource intensive. Future research will look at evaluation methodologies that take account of issues of consistency and ecological validity.

The method does not require training data and is easily scalable. The pipeline can be customized for individual use cases, for example, recall or precision enhancing.

The approach can provide centralized access to information from multiple domains currently provided by individual digital libraries.

The approach addresses metadata normalization in the context of web resources. The automatic classification approach accounts for matches within hierarchies, aggregating lower level matches to broader parents and thus approximates the practices of a human cataloger.

The purpose of this paper is to develop understanding of the problems of classification, to discover the classification practices of libraries with rich collections on Islam cited in the literature, to find the gaps, and to determine the point from which to start work on further development.

Published and unpublished literature, both print and electronic, that is relevant to the problem was reviewed objectively in the compilation of this paper.

Standard classification systems lack proper space for materials on Islam for two reasons: less awareness on the part of devisers of the depth and variety of Islamic topics; and their bias and lack of interest in Islam. Different indigenous classification systems and expansions have been developed, using either the original notation or alternative notations. Some systems have been developed without following any standards or logic. This study has revealed a need for empirical study of libraries with rich collections on Islam in order to gain a better understanding of the problem and find an optimal solution.

No empirical field data are included in this study. This is a review of the literature.

The author indicates the current situation of the problem and a potential framework for its solution.

Terms drawn from DDC indexes and IEEE Web Thesaurus were merged with DDC hierarchies to build a taxonomy in the domain of computer science. When displayed as a directory structure using a shareware tool MyInfo, the resultant taxonomy appeared to be a promising tool for categorisation that can facilitate browsing of information resources in an electronic environment.

The digital down converter (DDC) is a principal component in modern communication systems. The DDC process traditionally entails quadrature down conversion, bandwidth reducing filters and commensurate sample rate reduction. To avoid group delay, distortion linear phase FIR filters are used in the DDC. The filter performance specifications related to deep stopband attenuation, small in-band ripple and narrow transition bandwidth lead to filters with a large number of coefficients. To reduce the computational workload of the filtering process, filtering is often performed as a two-stage process, the first stage being a down sampling Hoegenauer (or cascade-integrated comb) filter and a reduced sample rate FIR filter. An alternative option is an M-Path polyphase partition of a band cantered FIR filter. Even though IIR filters offer reduced workload to implement a specific filtering task, the authors avoid using them because of their poor group delay characteristics. This paper aims to propose the design of M-path, approximately linear phase IIR filters as an alternative option to the M-path FIR filter.

Two filter designs are presented in the paper. The first approach uses linear phase IIR low pass structure to reduce the filter’s coefficient. Whereas the second approach uses multipath polyphase structure to design approximately linear phase IIR filter in DDC.

The authors have compared the performance and workload of the proposed polyphase structured IIR filters with state-of-the-art filter design used in DDC. The proposed design is seen to satisfy tight design specification with a significant reduction in arithmetic operations and required power consumption.

The proposed design is an alternate solution to the M-path polyphase FIR filter offering very less number of coefficients in the filter design. Proposed DDC using polyphase structured IIR filter satisfies the requirement of linear phase with the least number of computation cost in comparison with other DDC structure.

This paper aims to report on the design and development of a new approach for automatic classification and subject indexing of research documents in scientific digital libraries and repositories (DLR) according to library controlled vocabularies such as DDC and FAST.

The proposed concept matching-based approach (CMA) detects key Wikipedia concepts occurring in a document and searches the OPACs of conventional libraries via querying the WorldCat database to retrieve a set of MARC records which share one or more of the detected key concepts. Then the semantic similarity of each retrieved MARC record to the document is measured and, using an inference algorithm, the DDC classes and FAST subjects of those MARC records which have the highest similarity to the document are assigned to it.

The performance of the proposed method in terms of the accuracy of the DDC classes and FAST subjects automatically assigned to a set of research documents is evaluated using standard information retrieval measures of precision, recall, and F1. The authors demonstrate the superiority of the proposed approach in terms of accuracy performance in comparison to a similar system currently deployed in a large scale scientific search engine.

The proposed approach enables the development of a new type of subject classification system for DLR, and addresses some of the problems similar systems suffer from, such as the problem of imbalanced training data encountered by machine learning-based systems, and the problem of word-sense ambiguity encountered by string matching-based systems.

The purpose of this paper is to present a topic classification model using the Dewey Decimal Classification (DDC) as the target scheme. This is to be done by exploring metadata as provided by the Open Archives Initiative (OAI) to derive document snippets as minimal document representations. The reason is to reduce the effort of document processing in digital libraries. Further, the paper seeks to perform feature selection and extension by means of social ontologies and related web‐based lexical resources. This is done to provide reliable topic‐related classifications while circumventing the problem of data sparseness. Finally, the paper aims to evaluate the model by means of two language‐specific corpora. The paper bridges digital libraries, on the one hand, and computational linguistics, on the other. The aim is to make accessible computational linguistic methods to provide thematic classifications in digital libraries based on closed topic models such as the DDC.

The approach takes the form of text classification, text‐technology, computational linguistics, computational semantics, and social semantics.

It is shown that SVM‐based classifiers perform best by exploring certain selections of OAI document metadata.

The findings show that it is necessary to further develop SVM‐based DDC‐classifiers by using larger training sets possibly for more than two languages in order to get better F‐measure values.

Algorithmic and formal‐mathematical information is provided on how to build DDC‐classifiers for digital libraries.